Associative memories, also referred to as content addressable memories, are widely used in the field of pattern matching and identification, expert systems and artificial intelligence. A widely used associative memory is the Hopfield artificial neural network. Hopfield artificial neural networks are described, for example, in U.S. Pat. No. 4,660,166 to Hopfield entitled “Electronic Network for Collective Decision Based on Large Number of Connections Between Signals”.
Although associative memories may avoid problems in prior back-propagation networks, associative memories may present problems of scaling and spurious memories. Recent improvements in associative memories have attempted to solve these and other problems. For example, U.S. Pat. No. 6,052,679 to coinventor Aparicio, I V et al., entitled “Artificial Neural Networks Including Boolean-Complete Compartments” provides a plurality of artificial neurons and a plurality of Boolean-complete compartments, a respective one of which couples a respective pair of artificial neurons. By providing Boolean-complete compartments, spurious complement memories can be avoided.
Unfortunately, there is a fundamental scaling problem that can limit the use of associative memories to solve real world problems. In particular, many associative memories scale geometrically as a function of the number of inputs. This geometric scaling may be unreasonable to support applications at the scale of complexity that warrants such technology. Scaling in associative memories is addressed in U.S. Pat. No. 6,581,049 to coinventor Aparicio, I V et al., entitled “Artificial Neurons Including Power Series of Weights and Counts That Represent Prior and Next Associations”, and assigned to the assignee of the present invention, the disclosure of which is hereby incorporated herein by reference in its entirety as if set forth fully herein. As described in U.S. Pat. No. 6,581,049, an artificial neuron includes a plurality of inputs and a plurality of dendrites, a respective one of which is associated with a respective one of the plurality of inputs. Each dendrite comprises a power series of weights, and each weight in a power series includes an associated count for the associated power. By representing the weights as a power series, the geometric scaling as a function of input in conventional artificial neurons can be reduced to a linear scaling as a function of input. Large numbers of inputs may be handled using real world systems, to thereby solve real world applications.